Decreased water retention and increased runoff and soil loss are of special importance concerning soil degradation of hilly crop fields. In this study, plots under ploughing (conventional) tillage (PT) and conservation tillage (CT; 15 years) were compared. Rainfall simulation on 6 m² plots was applied to determine infiltration and soil loss during the growing season. Results were compared with those measured from 1200 m² plots exposed to natural rainfalls in 2016. Infiltration was always higher under CT than PT, whereas the highest infiltration was measured under the cover crop condition. Infiltration under seedbed and stubble resulted in uncertainties, which suggests that natural pore formation can be more effective at improving soil drainage potential than can temporary improvements created by soil tillage operations. Soil erodibility was higher under PT for each soil status; however, the seedbed condition triggered the highest values. For CT, soil loss volume was only a function of runoff volume at both scales. Contrarily, on PT plots, some extreme precipitation events triggered extremely high soil loss owing to linear erosion, which meant no direct connection existed between the scales. Improved soil conditions due to conservation practice are more important for decreasing soil loss than the better surface conditions.

Terrestrial biogeochemical models are essential tools to quantify climate–carbon cycle feedback and plant–soil relations from local to global scale. In this study, a theoretical basis is provided for the latest version of the Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. In this paper the improved soil hydrology as well as the soil carbon and nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology, soil nitrogen cycle, and soil organic carbon content estimation. Soil-hydrology-related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC and from 0.084 to 0.46 for soil evaporation; bias changed from −0.047 to −0.007 m³ m⁻³ for SWC and from −0.68 to −0.2 mm d⁻¹ for soil evaporation). Simulations related to nitrogen balance and soil CO₂ efflux were evaluated based on observations made in a long-term field experiment under crop rotation. The results indicated that the model is able to provide realistic nitrate content estimation for the topsoil. Soil nitrous oxide (N₂O) efflux and soil respiration simulations were also realistic, with overall correspondence with the observations (for the N₂O efflux simulation bias was between −0.13 and −0.1 $\mathrm{mg}\mathrm{N}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{d}}^{-\mathrm{1}}$ , and normalized root mean squared error (NRMSE) was 32.4 %–37.6 %; for CO₂ efflux simulations bias was 0.04–0.17 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{d}}^{-\mathrm{1}}$ , while NRMSE was 34.1 %–40.1 %). Sensitivity analysis and optimization of the decomposition scheme are presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation as well as nitrification and denitrification process estimation, which represents a major milestone.